1 Load environment

Code
import warnings

warnings.filterwarnings("ignore")

import matplotlib.pyplot as plt
import seaborn as sns
import scanpy as sc
import pandas as pd
import numpy as np
import random
import itertools

from tqdm import tqdm

import decoupler as dc
import sys

sys.path.append("./../../../../utilities_folder")
from utilities import load_object, intTable, plotGenesInTerm, getAnnGenes, run_ora_catchErrors

Set R environment with rpy2:

Code
import rpy2.rinterface_lib.callbacks
import anndata2ri
import logging

from rpy2.robjects import pandas2ri
import rpy2.robjects as ro

sc.settings.verbosity = 0
rpy2.rinterface_lib.callbacks.logger.setLevel(logging.ERROR)

pandas2ri.activate()
anndata2ri.activate()

%load_ext rpy2.ipython

Set up of graphical parameters for Python plots:

Code
%matplotlib inline
sc.set_figure_params(dpi = 300, fontsize = 20)

cmap_up = sns.light_palette("red", as_cmap=True)
cmap_down = sns.light_palette("blue", as_cmap=True)
cmap_all = sns.light_palette("seagreen", as_cmap=True)

Set up of graphical parameters for R plots:

Code
default_units = 'in' 
default_res = 300
default_width = 10
default_height = 9

import rpy2
old_setup_graphics = rpy2.ipython.rmagic.RMagics.setup_graphics

def new_setup_graphics(self, args):
    if getattr(args, 'units') is not None:
        if args.units != default_units:
            return old_setup_graphics(self, args)
    args.units = default_units
    if getattr(args, 'res') is None:
        args.res = default_res
    if getattr(args, 'width') is None:
        args.width = default_width
    if getattr(args, 'height') is None:
        args.height = default_height        
    return old_setup_graphics(self, args)


rpy2.ipython.rmagic.RMagics.setup_graphics = new_setup_graphics

Here the cell were we inject the parameters using Quarto renderer:

Code
# Injected Parameters
N = 3
Code
# Injected Parameters
N = 5

Import R libraries:

Code
%%R
source('./../../../../utilities_folder/GO_helper.r')
loc <- './../../../../R_loc' # pointing to the renv environment

.libPaths(loc)

library('topGO')
library('org.Hs.eg.db')
library(dplyr)
library(ggplot2)

Set output folders:

Code
output_folder = './'
folder = './deg_in_cluster_tables/cluster_' + str(N) + '/'

import os

if not os.path.isdir(folder):
    os.makedirs(folder)

2 Load data

Here we load the anndata:

Code
GO2gene = load_object('./../../../../data/GO2gene_complete.pickle')

Here we load the dictionary that associates to each GO term its genes:

Code
adata = sc.read("1_All_Annotated_Triku.h5ad")
adata
AnnData object with n_obs × n_vars = 27925 × 14582
    obs: 'sample_id', 'run_id', 'probe_barcode_ids', 'subject', 'line', 'specimen', 'stage', 'condition', 'notes', 'seqRun', 'original_name', 'project', 'who', 'SC_derivation', 'micoplasma', 'mosaic', 'genSite', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mito', 'log1p_total_counts_mito', 'pct_counts_mito', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'gene_UMI_ratio', 'log1p_gene_UMI_ratio', 'scDblFinder_score', 'scDblFinder_class', 'n_counts', 'n_genes', 'leiden', 'score_pluripotency', 'score_mesoderm', 'score_PS', 'score_EM', 'score_AM', 'score_Endo', 'score_Epi', 'score_HEP', 'score_NM', 'score_ExM', 'score_Ery', 'score_EAE', 'score_Amnion_LC', 'score_AxM', 'score_Amnion', 'score_NNE', 'score_PGC', 'score_hPGCLCs', 'score_DE1', 'score_DE2', 'score_Hypoblast', 'score_YS', 'score_EMPs', 'score_Endothelium', 'score_Myeloid_Progenitors', 'score_Megakaryocyte_Erythroid_progenitors', 'CellTypes'
    var: 'n_cells', 'mito', 'ribo', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection', 'triku_distance', 'triku_distance_uncorrected', 'triku_highly_variable'
    uns: 'CellTypes_colors', 'dendrogram_CellTypes', 'dendrogram_leiden', 'dendrogram_specimen', 'hvg', 'leiden', 'leiden_colors', 'log1p', 'neighbors', 'pca', 'rank_genes_groups', 'run_id_colors', 'sample_id_colors', 'specimen_colors', 'stage_colors', 'triku_params', 'umap'
    obsm: 'X_pca', 'X_pca_harmony', 'X_umap'
    varm: 'PCs'
    layers: 'raw'
    obsp: 'connectivities', 'distances'

3 Markers of cluster

We filter genes for the cluster under investigation based on the p-value adjusted that we then convert in -log(p-value adjusted):

Code
markers = sc.get.rank_genes_groups_df(adata, group=str(N))
intTable(markers, folder=folder, fileName = 'markers_cluster_before_filtering_' + str(N) + '.xlsx', save = True)
Code
markers = markers.set_index('names')
markers = markers[markers.pvals_adj < 0.01]
markers['FDR'] = markers['pvals_adj']
markers['-log10(FDR)'] = -np.log10(markers.pvals_adj)
markers = markers.replace(np.inf, markers[markers['-log10(FDR)'] != np.inf]['-log10(FDR)'].max())
markers
scores logfoldchanges pvals pvals_adj pct_nz_group pct_nz_reference FDR -log10(FDR)
names
COBLL1 76.434547 6.237903 0.0 0.0 0.994545 0.218426 0.0 308.636343
SLC9A3R1 75.939949 4.212123 0.0 0.0 0.998636 0.872847 0.0 308.636343
HAND1 74.259132 6.656892 0.0 0.0 1.000000 0.214888 0.0 308.636343
AQP1 74.183632 8.237256 0.0 0.0 0.964091 0.087580 0.0 308.636343
MYRF 74.077148 5.166291 0.0 0.0 0.995455 0.320389 0.0 308.636343
... ... ... ... ... ... ... ... ...
IGFBP2 -61.813698 -5.061126 0.0 0.0 0.269091 0.854227 0.0 308.636343
TRIM71 -62.067471 -3.621088 0.0 0.0 0.490000 0.908222 0.0 308.636343
LRRN1 -62.209316 -4.098814 0.0 0.0 0.276364 0.884354 0.0 308.636343
CTSC -62.891335 -2.657286 0.0 0.0 0.694091 0.958873 0.0 308.636343
SFRP2 -63.084988 -7.637330 0.0 0.0 0.046364 0.820836 0.0 308.636343

10859 rows × 8 columns

Code
intTable(markers, folder=folder, fileName = 'markers_cluster_filtered_' + str(N) + '.xlsx', save = True)

3.1 Select up and downregulated markers

3.1.1 Up

Code
pos = markers[markers.logfoldchanges > 1]
pos
scores logfoldchanges pvals pvals_adj pct_nz_group pct_nz_reference FDR -log10(FDR)
names
COBLL1 76.434547 6.237903 0.000000 0.000000 0.994545 0.218426 0.000000 308.636343
SLC9A3R1 75.939949 4.212123 0.000000 0.000000 0.998636 0.872847 0.000000 308.636343
HAND1 74.259132 6.656892 0.000000 0.000000 1.000000 0.214888 0.000000 308.636343
AQP1 74.183632 8.237256 0.000000 0.000000 0.964091 0.087580 0.000000 308.636343
MYRF 74.077148 5.166291 0.000000 0.000000 0.995455 0.320389 0.000000 308.636343
... ... ... ... ... ... ... ... ...
SERHL2 2.724010 1.461655 0.006449 0.008705 0.057727 0.023013 0.008705 2.060244
CYP7B1 2.723756 1.037613 0.006454 0.008711 0.069091 0.034091 0.008711 2.059951
PRPH 2.716357 2.128287 0.006600 0.008900 0.046818 0.012089 0.008900 2.050595
TMEM121 2.698906 1.198835 0.006957 0.009363 0.064091 0.029621 0.009363 2.028603
CFAP206 2.679350 2.160276 0.007377 0.009908 0.045000 0.010690 0.009908 2.004001

1471 rows × 8 columns

Code
pos['-log10(FDR)'] = -np.log10(pos.pvals_adj)
#pos = pos.fillna('inf')
pos = pos.replace(np.inf, pos[pos['-log10(FDR)'] != np.inf]['-log10(FDR)'].max())
pos['FDR'] = pos['pvals_adj']
up_regGenes = pos.index.tolist()
allUpSelected = adata.var_names.isin(up_regGenes).astype('int').tolist()

3.1.2 Down

Code
neg = markers[markers.logfoldchanges < -1].sort_values(by = 'logfoldchanges')
neg
scores logfoldchanges pvals pvals_adj pct_nz_group pct_nz_reference FDR -log10(FDR)
names
BSPRY -12.518145 -27.832422 5.940772e-36 1.648494e-35 0.000000 0.160544 1.648494e-35 34.782913
HTR1D -10.296401 -27.807291 7.314678e-25 1.743423e-24 0.000000 0.132051 1.743423e-24 23.758597
PPP1R17 -8.847569 -27.376715 8.944558e-19 1.934582e-18 0.000000 0.113469 1.934582e-18 17.713413
BICDL2 -9.147642 -27.317181 5.818994e-20 1.287205e-19 0.000000 0.117318 1.287205e-19 18.890352
GLIPR1L1 -6.889526 -27.257271 5.597858e-12 1.047318e-11 0.000000 0.088358 1.047318e-11 10.979921
... ... ... ... ... ... ... ... ...
COQ10A -12.417840 -1.001408 2.091330e-35 5.757179e-35 0.205909 0.365131 5.757179e-35 34.239790
SOCS1 -21.267248 -1.000872 2.282897e-100 1.147904e-99 0.562727 0.738387 1.147904e-99 98.940095
SCAI -15.014860 -1.000431 5.868572e-51 1.923910e-50 0.287273 0.469504 1.923910e-50 49.715815
EDA2R -7.561934 -1.000399 3.971219e-14 7.818052e-14 0.101364 0.200855 7.818052e-14 13.106901
ANKHD1 -28.175198 -1.000084 1.177541e-174 9.828791e-174 0.710455 0.868805 9.828791e-174 173.007500

3245 rows × 8 columns

Code
neg['-log10(FDR)'] = -np.log10(neg.pvals_adj)
#neg = neg.fillna('inf')
neg = neg.replace(np.inf, neg[neg['-log10(FDR)'] != np.inf]['-log10(FDR)'].max())
neg['FDR'] = neg['pvals_adj']
down_regGenes = neg.index.tolist()
allDownSelected = adata.var_names.isin(down_regGenes).astype('int').tolist()

3.1.3 All regulated

Code
all_sign = up_regGenes + down_regGenes
allSelected = adata.var_names.isin(all_sign).astype('int').tolist()
allGenes = adata.var_names.tolist()

4 topGO

4.1 Upregulated

Code
%%R -i allUpSelected -i allGenes

allGenes_v <- c(allUpSelected)
#print(allGenes_v)
names(allGenes_v) <- allGenes
allGenes_v <- unlist(allGenes_v)

geneNames <- c(allGenes)

ann_org_BP <- topGO::annFUN.org(whichOnto='BP', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_MF <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_CC <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

selection <- function(allScores){return (as.logical(allScores))}

Get topGO results:

Code
%%R -o results

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_BP,
  geneSel = selection,
  nodeSize=10)

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Convert results from R environment to Python:

Code
scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData
array([13071,  1337,    10,  5638], dtype=int32)
Code
#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

results_table_py = ro.conversion.rpy2py(results_table)

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py
GO.ID Term Annotated Significant Expected weight Scores
0 GO:0048844 artery morphogenesis 67 30 6.85 9.0e-12 8.998400e-12
1 GO:0030199 collagen fibril organization 60 26 6.14 3.2e-11 3.188841e-11
2 GO:0007155 cell adhesion 1176 244 120.29 2.0e-10 2.043880e-10
3 GO:0055010 ventricular cardiac muscle tissue morpho... 40 17 4.09 2.6e-10 2.602061e-10
4 GO:0030198 extracellular matrix organization 267 89 27.31 2.9e-10 2.948095e-10
... ... ... ... ... ... ... ...
6269 GO:2000672 negative regulation of motor neuron apop... 11 0 1.13 1.00000 1.000000e+00
6270 GO:2000765 regulation of cytoplasmic translation 26 0 2.66 1.00000 1.000000e+00
6271 GO:2000767 positive regulation of cytoplasmic trans... 13 0 1.33 1.00000 1.000000e+00
6272 GO:2000786 positive regulation of autophagosome ass... 18 0 1.84 1.00000 1.000000e+00
6273 GO:2001014 regulation of skeletal muscle cell diffe... 17 0 1.74 1.00000 1.000000e+00

6274 rows × 7 columns

Filter results:

Code
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

results_table_py['-log10(pvalue)'] = - np.log10(results_table_py.Scores)
results_table_py['Significant/Annotated'] = results_table_py['Significant'] / results_table_py['Annotated']
Code
intTable(results_table_py, folder = folder, fileName = 'GO_BP_up.xlsx', save = True)
Code
%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'BP', fillCol = 'red')

Code
%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='red', log = TRUE)

Code
%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_BP_genesInTerm_up.xlsx'), SE = markers)
Code
%%R

GOdata <- new("topGOdata",
  ontology="MF",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_MF,
  geneSel = selection,
  nodeSize=10)
Code
%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")
Code
scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData
array([13424,  1364,    10,   974], dtype=int32)
Code
#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))
Code
results_table_py = ro.conversion.rpy2py(results_table)
Code
scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]
Code
scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_MF_up.xlsx', save = True)
Code
%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'MF', fillCol = 'red')

Code
%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='red', log = TRUE)

Code
%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_MF_genesInTerm_up.xlsx'), SE = markers)
Code
%%R

GOdata <- new("topGOdata",
  ontology="CC",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_CC,
  geneSel = selection,
  nodeSize=10)
Code
%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")
Code
scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData
array([13666,  1402,    10,   651], dtype=int32)
Code
#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))
Code
results_table_py = ro.conversion.rpy2py(results_table)
Code
scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]
Code
scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
Code
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_CC_up.xlsx', save = True)
Code
%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'CC', fillCol = 'red')

Code
%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='red', log = TRUE)

Code
%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_CC_genesInTerm_up.xlsx'), SE = markers)

4.1.0.1 Reactome

Code
curated = msigdb[msigdb['collection'].isin(['reactome_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()
Code
rank = pd.DataFrame(markers['-log10(FDR)'])

rank_copy = rank.copy()
rank_copy['pval'] = markers.loc[rank.index].FDR
Code
rank_copy
-log10(FDR) pval
names
COBLL1 308.636343 0.0
SLC9A3R1 308.636343 0.0
HAND1 308.636343 0.0
AQP1 308.636343 0.0
MYRF 308.636343 0.0
... ... ...
IGFBP2 308.636343 0.0
TRIM71 308.636343 0.0
LRRN1 308.636343 0.0
CTSC 308.636343 0.0
SFRP2 308.636343 0.0

10859 rows × 2 columns

Code
results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
len(results_table_py)
877
Code
intTable(results_table_py, folder = folder, fileName = 'Reactome_up.xlsx', save = True)
Code
if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['reactome_pathways'], rank_copy)
    _, df = plotGenesInTerm(results = results_table_py, 
                            GO2gene = GO2gene['reactome_pathways'], DEGs = rank_copy, n_top_terms = 10, cmap = cmap_up)

Code
if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_Reactome_up.xlsx', save = True)

4.1.0.2 KEGG

Code
curated = msigdb[msigdb['collection'].isin(['kegg_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()
results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
Code
intTable(results_table_py, folder = folder, fileName = 'KEGG_up.xlsx', save = True)
Code
if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['kegg_pathways'], rank_copy)
    _, df = plotGenesInTerm(results_table_py, GO2gene['kegg_pathways'], rank_copy, n_top_terms = 10, n_top_genes = 15, cmap = cmap_up)

Code
if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_KEGG_up.xlsx', save = True)

4.2 Downregulated

Code
%%R -i allDownSelected -i allGenes

allGenes_v <- c(allDownSelected)
#print(allGenes_v)
names(allGenes_v) <- allGenes
allGenes_v <- unlist(allGenes_v)

geneNames <- c(allGenes)

ann_org_BP <- topGO::annFUN.org(whichOnto='BP', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_MF <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_CC <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

selection <- function(allScores){return (as.logical(allScores))}
Code
%%R
#print(lapply(ann_org_BP, count_genes))

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_BP,
  geneSel = selection,
  nodeSize=10)
Code
%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")
Code
scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData
array([13071,  2929,    10,  6166], dtype=int32)
Code
#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))
Code
results_table_py = ro.conversion.rpy2py(results_table)
Code
scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]
Code
scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py
GO.ID Term Annotated Significant Expected weight Scores
0 GO:0006268 DNA unwinding involved in DNA replicatio... 22 16 4.93 7.1e-07 7.125538e-07
1 GO:0006271 DNA strand elongation involved in DNA re... 15 12 3.36 3.6e-06 3.589136e-06
2 GO:0007094 mitotic spindle assembly checkpoint sign... 46 28 10.31 9.3e-06 9.344430e-06
3 GO:0006188 IMP biosynthetic process 10 9 2.24 1.1e-05 1.127170e-05
4 GO:0051301 cell division 575 173 128.85 1.9e-05 1.896621e-05
... ... ... ... ... ... ... ...
6269 GO:2000303 regulation of ceramide biosynthetic proc... 11 0 2.46 1.00000 1.000000e+00
6270 GO:2000434 regulation of protein neddylation 17 0 3.81 1.00000 1.000000e+00
6271 GO:2000601 positive regulation of Arp2/3 complex-me... 10 0 2.24 1.00000 1.000000e+00
6272 GO:2000786 positive regulation of autophagosome ass... 18 0 4.03 1.00000 1.000000e+00
6273 GO:2000826 regulation of heart morphogenesis 10 0 2.24 1.00000 1.000000e+00

6274 rows × 7 columns

Code
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]
Code
results_table_py['-log10(pvalue)'] = - np.log10(results_table_py.Scores)
results_table_py['Significant/Annotated'] = results_table_py['Significant'] / results_table_py['Annotated']
Code
intTable(results_table_py, folder = folder, fileName = 'GO_BP_down.xlsx', save = True)
Code
%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'BP', fillCol = 'blue')

Code
%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='blue', log = TRUE)

Code
%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_BP_genesInTerm_down.xlsx'), SE = markers)
Code
%%R

GOdata <- new("topGOdata",
  ontology="MF",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_MF,
  geneSel = selection,
  nodeSize=10)
Code
%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")
Code
scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData
array([13424,  2991,    10,  1139], dtype=int32)
Code
#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))
Code
results_table_py = ro.conversion.rpy2py(results_table)
Code
scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]
Code
scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_MF_down.xlsx', save = True)
Code
%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'MF', fillCol = 'blue')

Code
%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='blue', log = TRUE)

Code
%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_MF_genesInTerm_down.xlsx'), SE = markers)
Code
%%R

GOdata <- new("topGOdata",
  ontology="CC",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_CC,
  geneSel = selection,
  nodeSize=10)
Code
%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")
Code
scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData
array([13666,  3046,    10,   780], dtype=int32)
Code
#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))
Code
results_table_py = ro.conversion.rpy2py(results_table)
Code
scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]
Code
scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
Code
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_CC_down.xlsx', save = True)
Code
%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'CC', fillCol = 'blue')

Code
%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='blue', log = TRUE)

Code
%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_CC_genesInTerm_down.xlsx'), SE = markers)

4.2.0.1 Reactome

Code
curated = msigdb[msigdb['collection'].isin(['reactome_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()
Code
rank = pd.DataFrame(markers['-log10(FDR)'])

rank_copy = rank.copy()
rank_copy['pval'] = markers.loc[rank.index].FDR
Code
rank_copy
-log10(FDR) pval
names
COBLL1 308.636343 0.0
SLC9A3R1 308.636343 0.0
HAND1 308.636343 0.0
AQP1 308.636343 0.0
MYRF 308.636343 0.0
... ... ...
IGFBP2 308.636343 0.0
TRIM71 308.636343 0.0
LRRN1 308.636343 0.0
CTSC 308.636343 0.0
SFRP2 308.636343 0.0

10859 rows × 2 columns

Code
results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
len(results_table_py)
877
Code
intTable(results_table_py, folder = folder, fileName = 'Reactome_down.xlsx', save = True)
Code
if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['reactome_pathways'], rank_copy)
    _, df = plotGenesInTerm(results = results_table_py, GO2gene = GO2gene['reactome_pathways'], DEGs = rank_copy, n_top_terms = 10, cmap = cmap_down)

Code
if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_Reactome_down.xlsx', save = True)

4.2.0.2 KEGG

Code
curated = msigdb[msigdb['collection'].isin(['kegg_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()
Code
results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
Code
intTable(results_table_py, folder = folder, fileName = 'KEGG_down.xlsx', save = True)
Code
if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['kegg_pathways'], rank_copy)
    _, df = plotGenesInTerm(results_table_py, GO2gene['kegg_pathways'], rank_copy, n_top_terms = 10, n_top_genes = 15, cmap = cmap_down)

Code
if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_KEGG_down.xlsx', save = True)

4.3 All significant

Code
%%R -i allSelected -i allGenes

allGenes_v <- c(allUpSelected)
#print(allGenes_v)
names(allGenes_v) <- allGenes
allGenes_v <- unlist(allGenes_v)

geneNames <- c(allGenes)

ann_org_BP <- topGO::annFUN.org(whichOnto='BP', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_MF <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_CC <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

selection <- function(allScores){return (as.logical(allScores))}
Code
%%R
#print(lapply(ann_org_BP, count_genes))

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_BP,
  geneSel = selection,
  nodeSize=10)
Code
%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")
Code
scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData
array([13071,  1337,    10,  5638], dtype=int32)
Code
#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))
Code
results_table_py = ro.conversion.rpy2py(results_table)
Code
scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]
Code
scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py
GO.ID Term Annotated Significant Expected weight Scores
0 GO:0048844 artery morphogenesis 67 30 6.85 9.0e-12 8.998400e-12
1 GO:0030199 collagen fibril organization 60 26 6.14 3.2e-11 3.188841e-11
2 GO:0007155 cell adhesion 1176 244 120.29 2.0e-10 2.043880e-10
3 GO:0055010 ventricular cardiac muscle tissue morpho... 40 17 4.09 2.6e-10 2.602061e-10
4 GO:0030198 extracellular matrix organization 267 89 27.31 2.9e-10 2.948095e-10
... ... ... ... ... ... ... ...
6269 GO:2000672 negative regulation of motor neuron apop... 11 0 1.13 1.00000 1.000000e+00
6270 GO:2000765 regulation of cytoplasmic translation 26 0 2.66 1.00000 1.000000e+00
6271 GO:2000767 positive regulation of cytoplasmic trans... 13 0 1.33 1.00000 1.000000e+00
6272 GO:2000786 positive regulation of autophagosome ass... 18 0 1.84 1.00000 1.000000e+00
6273 GO:2001014 regulation of skeletal muscle cell diffe... 17 0 1.74 1.00000 1.000000e+00

6274 rows × 7 columns

Code
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]
Code
results_table_py['-log10(pvalue)'] = - np.log10(results_table_py.Scores)
results_table_py['Significant/Annotated'] = results_table_py['Significant'] / results_table_py['Annotated']
Code
intTable(results_table_py, folder = folder, fileName = 'GO_BP_all.xlsx', save = True)
Code
%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'BP', fillCol = 'forestgreen')

Code
%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code
%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_BP_genesInTerm_all.xlsx'), SE = markers)
Code
%%R

GOdata <- new("topGOdata",
  ontology="MF",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_MF,
  geneSel = selection,
  nodeSize=10)
Code
%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")
Code
scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData
array([13424,  1364,    10,   974], dtype=int32)
Code
#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))
Code
results_table_py = ro.conversion.rpy2py(results_table)
Code
scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]
Code
scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_MF_all.xlsx', save = True)
Code
%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'MF', fillCol = 'forestgreen')

Code
%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code
%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_MF_genesInTerm_all.xlsx'), SE = markers)
Code
%%R

GOdata <- new("topGOdata",
  ontology="CC",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_CC,
  geneSel = selection,
  nodeSize=10)
Code
%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")
Code
scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData
array([13666,  1402,    10,   651], dtype=int32)
Code
#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))
Code
results_table_py = ro.conversion.rpy2py(results_table)
Code
scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]
Code
scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
Code
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_CC_all.xlsx', save = True)
Code
%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'CC', fillCol = 'forestgreen')

Code
%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code
%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_CC_genesInTerm_all.xlsx'), SE = markers)

4.3.0.1 Reactome

Code
curated = msigdb[msigdb['collection'].isin(['reactome_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()
Code
rank = pd.DataFrame(markers['-log10(FDR)'])

rank_copy = rank.copy()
rank_copy['pval'] = markers.loc[rank.index].FDR
Code
rank_copy
-log10(FDR) pval
names
COBLL1 308.636343 0.0
SLC9A3R1 308.636343 0.0
HAND1 308.636343 0.0
AQP1 308.636343 0.0
MYRF 308.636343 0.0
... ... ...
IGFBP2 308.636343 0.0
TRIM71 308.636343 0.0
LRRN1 308.636343 0.0
CTSC 308.636343 0.0
SFRP2 308.636343 0.0

10859 rows × 2 columns

Code
results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
len(results_table_py)
877
Code
intTable(results_table_py, folder = folder, fileName = 'Reactome_all.xlsx', save = True)
Code
if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['reactome_pathways'], rank_copy)
    _, df = plotGenesInTerm(results = results_table_py, GO2gene = GO2gene['reactome_pathways'], DEGs = rank_copy, n_top_terms = 10, cmap = cmap_all)

Code
if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_Reactome_all.xlsx', save = True)

4.3.0.2 KEGG

Code
curated = msigdb[msigdb['collection'].isin(['kegg_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()
Code
results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
Code
intTable(results_table_py, folder = folder, fileName = 'KEGG_all.xlsx', save = True)
Code
if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['kegg_pathways'], rank_copy)
    _, df = plotGenesInTerm(results_table_py, GO2gene['kegg_pathways'], rank_copy, n_top_terms = 10, n_top_genes = 15, cmap = cmap_all)

Code
if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_KEGG_all.xlsx', save = True)